Multi-scale Microstructure Prediction in Thermo-mechanical Processing

This research project aims at (i) establishing a systematic methodology for predicting the microstructure evolution during thermo-mechanical processing in metals by modeling at multiple length scales, (ii) developing and implementing the method in the form of efficient numerical algorithms, and (iii) applying the algorithms to study the fundamental microstructural phenomena of grain growth and recrystallization. This approach is superior to the empirical modeling techniques prevalent in current industrial practice.

The proposed multi-scale methodology will be implemented into a simulation system that

consists of continuum-based coupled thermo-mechanical models, a multi-scale modeling

interface and mesoscopic microstructural models. The simulation input includes the processing conditions, macroscopic and mesoscopic material properties and initial microstructure features. The final output of the simulation sequence is the resulting microstructure.

The proposed three-year project will comprise three major phases:

Phase I, involves (i) the development of a model for computing the stored energy as a function of strain and stress, (ii) the development of two- and three-dimensional Monte Carlo (MC) models for simulating static recrystallization and normal grain growth, and (iii) the application of these models to study the fundamental physics of various microstructural phenomena. In Phase II of this project, the length and time scale conversions between the physical and MC domains will be performed by conducting grain growth experiments with a pure material. In Phase III, cold upsetting and subsequent annealing tests of a pure material will be carried out. The grain sizes measured in these experiments will be used to validate the proposed

methodology of microstructure prediction through multi-scale modeling. Through the development, implementation and validation of a multi-scale approach for modeling microstructure phenomena in metals, this project will advance the state of the art in microstructure prediction in an industrially relevant way. An industry collaborator (McWilliams Forge Co.) will participate in the project. Furthermore, both undergraduate and graduate students at Stevens Institute of Technology (SIT) will be involved in the research, with an strong effort made towards including female and underrepresented minority students.